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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
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Taping Over Different Ground Profiles01:12

Taping Over Different Ground Profiles

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Taping over varying ground profiles requires careful adaptation to achieve accurate measurements. On smooth, level ground with minimal vegetation, the tape can rest directly on the ground. Here, the taping team, typically consisting of a head and a rear tapeman, coordinates their positions with clear communication. The rear tapeman holds the tape at the starting point and guides the head tapeman toward a range pole placed beyond the endpoint, using hand or voice signals to ensure alignment.On...
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Profile Leveling and Cross Sections01:26

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Profile leveling and cross-sections are surveying methods used to determine and document terrain elevations for infrastructure projects such as highways, railroads, canals, and pipelines. These methods provide data for earthwork planning and alignment of proposed routes.  Profile leveling involves measuring elevations along a fixed line to create a vertical terrain profile. A surveyor sets up a leveling instrument at the benchmark (BM) and records a backsight (BS) to determine the...
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Drug Dissolution: Requirements and Profile Comparison01:14

Drug Dissolution: Requirements and Profile Comparison

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The acceptance criteria for dissolution profile data are anchored in Q values, representing the percentage of drug dissolved within a specified period. This assessment unfolds in three stages:First Stage: The test passes if all six drug dosage units are equal to or greater than Q plus 5%; otherwise, the sample proceeds to the second stage.Second Stage: The average of twelve units must be equal to or greater than Q, with no unit falling below Q - 15% to pass; if not, it progresses to the final...
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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Related Experiment Video

Updated: Jan 20, 2026

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data
07:17

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Published on: February 7, 2025

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Profile Hidden Markov Models Are Not Identifiable.

Srilakshmi Pattabiraman, Tandy Warnow

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |August 20, 2019
    PubMed
    Summary
    This summary is machine-generated.

    Profile Hidden Markov Models (HMMs) are widely used in bioinformatics for sequence analysis. This study investigates whether these statistical models are uniquely identifiable, a fundamental property not yet established for HMMs.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Statistical Modeling

    Background:

    • Profile Hidden Markov Models (HMMs) are a cornerstone statistical tool in bioinformatics, essential for tasks like protein analysis and sequence alignment.
    • Despite their widespread application for 25 years, the statistical identifiability of profile HMMs remains an open question.
    • Identifiability ensures that a unique model corresponds to a given data distribution, crucial for reliable analysis.

    Purpose of the Study:

    • To investigate the statistical identifiability of profile Hidden Markov Models (HMMs).
    • To address the fundamental question of whether distinct profile HMMs can generate identical sequence distributions.
    • To contribute preliminary findings on the identifiability of standard profile HMM forms used in bioinformatics.

    Main Methods:

    • The study focuses on the statistical properties of profile HMMs.
    • It examines the conditions under which a profile HMM can be uniquely determined from sequence data.
    • Preliminary theoretical analysis is applied to standard profile HMM structures.

    Main Results:

    • The research explores the statistical identifiability of profile Hidden Markov Models (HMMs).
    • It addresses whether unique models can be inferred from sequence data distributions.
    • Preliminary results are presented regarding the identifiability of common profile HMMs.

    Conclusions:

    • The statistical identifiability of profile HMMs is a critical but underexplored area in bioinformatics.
    • Understanding identifiability is key to ensuring the robustness of bioinformatics predictions.
    • This work provides initial insights into the identifiability of profile HMMs, paving the way for future research.